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首页> 外文会议>IEEE International Conference on Neural Networks >Learning critical temperature for homomorphic ARG matching by self-organising Hopfield network
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Learning critical temperature for homomorphic ARG matching by self-organising Hopfield network

机译：自组织Hopfield网络学习同性恋争吵的临界温度

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摘要

The authors previously (1995) presented a programming strategy to generate a homomorphic mapping between two attributed relational graphs (ARG) by the Hopfield network. Further, a self-organisation scheme was also introduced to learn the constraint parameter used in the energy function. In order to generate the desired mapping, the temperature parameter should be set to the critical value. Estimation of the critical temperature is an extremely difficult problem. In this paper, a heuristic learning algorithm is presented to estimate a suitable value for the temperature parameter for every model and scene pair to be matched. Experimental results showed that the learning algorithm is capable of compensating for the variations in the model and scene characteristics and the time step used to simulate the dynamic equations of the Hopfield network.
机译：此前（1995）的作者提出了一种编程策略，以通过Hopfield网络生成两个属性关系图（ARG）之间的同态映射。此外，还引入了自组织方案以学习能量函数中使用的约束参数。为了生成所需的映射，应将温度参数设置为临界值。临界温度的估计是一个非常困难的问题。在本文中，提出了一种启发式学习算法以估计要匹配的每个模型和场景对的温度参数的合适值。实验结果表明，学习算法能够补偿模型和场景特性的变化以及用于模拟Hopfield网络的动态方程的时间步长。

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